State-of-the-Art Analysis of Modern Drowsiness Detection Algorithms Based on Computer Vision
The paper presented a state-of-the-art analysis of modern drowsiness detection algorithms based on computer vision technologies as well as consider the problem of yawning detection for the vehicle driver. Based on the literature analysis we classify drowsiness detection techniques into three groups:...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
FRUCT
2021-05-01
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| Series: | Proceedings of the XXth Conference of Open Innovations Association FRUCT |
| Subjects: | |
| Online Access: | https://www.fruct.org/publications/fruct29/files/Has.pdf |
| _version_ | 1818736581274501120 |
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| author | Fudail Hasan Alexey Kashevnik |
| author_facet | Fudail Hasan Alexey Kashevnik |
| author_sort | Fudail Hasan |
| collection | DOAJ |
| description | The paper presented a state-of-the-art analysis of modern drowsiness detection algorithms based on computer vision technologies as well as consider the problem of yawning detection for the vehicle driver. Based on the literature analysis we classify drowsiness detection techniques into three groups: the driving pattern of the vehicle; psychophysiological characteristics of drivers; and computer vision techniques for driver monitoring. So, the computer vision methods look most promising since they are non-intrusive for the driver. The importance of the driver drowsiness monitoring system is due to the number of drowsiness-related accidents. Yawning is an important identifier of drowsiness, even it is not the most reliable drowsiness indicator. Some of the methods that are based on computer vision are presented and discussed in the paper. We developed and evaluated a yawning detection model. We analyzed available datasets for yawning detection and conclude that the existing datasets have to be enhanced by pictures taken in real driving conditions. We propose yawning detection dataset-preparation as well as detection model development and evaluation. |
| first_indexed | 2024-12-18T00:39:25Z |
| format | Article |
| id | doaj.art-9641e0f70e9044c789b50585fd6ec419 |
| institution | Directory Open Access Journal |
| issn | 2305-7254 2343-0737 |
| language | English |
| last_indexed | 2024-12-18T00:39:25Z |
| publishDate | 2021-05-01 |
| publisher | FRUCT |
| record_format | Article |
| series | Proceedings of the XXth Conference of Open Innovations Association FRUCT |
| spelling | doaj.art-9641e0f70e9044c789b50585fd6ec4192022-12-21T21:26:56ZengFRUCTProceedings of the XXth Conference of Open Innovations Association FRUCT2305-72542343-07372021-05-0129114114910.23919/FRUCT52173.2021.9435480State-of-the-Art Analysis of Modern Drowsiness Detection Algorithms Based on Computer VisionFudail Hasan0Alexey Kashevnik1ITMO Univesity, RussiaITMO Univesity, RussiaThe paper presented a state-of-the-art analysis of modern drowsiness detection algorithms based on computer vision technologies as well as consider the problem of yawning detection for the vehicle driver. Based on the literature analysis we classify drowsiness detection techniques into three groups: the driving pattern of the vehicle; psychophysiological characteristics of drivers; and computer vision techniques for driver monitoring. So, the computer vision methods look most promising since they are non-intrusive for the driver. The importance of the driver drowsiness monitoring system is due to the number of drowsiness-related accidents. Yawning is an important identifier of drowsiness, even it is not the most reliable drowsiness indicator. Some of the methods that are based on computer vision are presented and discussed in the paper. We developed and evaluated a yawning detection model. We analyzed available datasets for yawning detection and conclude that the existing datasets have to be enhanced by pictures taken in real driving conditions. We propose yawning detection dataset-preparation as well as detection model development and evaluation.https://www.fruct.org/publications/fruct29/files/Has.pdfdriver state monitoringdriver drowsiness detectionyawning detectiondeep learning |
| spellingShingle | Fudail Hasan Alexey Kashevnik State-of-the-Art Analysis of Modern Drowsiness Detection Algorithms Based on Computer Vision Proceedings of the XXth Conference of Open Innovations Association FRUCT driver state monitoring driver drowsiness detection yawning detection deep learning |
| title | State-of-the-Art Analysis of Modern Drowsiness Detection Algorithms Based on Computer Vision |
| title_full | State-of-the-Art Analysis of Modern Drowsiness Detection Algorithms Based on Computer Vision |
| title_fullStr | State-of-the-Art Analysis of Modern Drowsiness Detection Algorithms Based on Computer Vision |
| title_full_unstemmed | State-of-the-Art Analysis of Modern Drowsiness Detection Algorithms Based on Computer Vision |
| title_short | State-of-the-Art Analysis of Modern Drowsiness Detection Algorithms Based on Computer Vision |
| title_sort | state of the art analysis of modern drowsiness detection algorithms based on computer vision |
| topic | driver state monitoring driver drowsiness detection yawning detection deep learning |
| url | https://www.fruct.org/publications/fruct29/files/Has.pdf |
| work_keys_str_mv | AT fudailhasan stateoftheartanalysisofmoderndrowsinessdetectionalgorithmsbasedoncomputervision AT alexeykashevnik stateoftheartanalysisofmoderndrowsinessdetectionalgorithmsbasedoncomputervision |